Genetic Algoithms and Genetic Programming

The application uses Genetic Algorithm to elicit the target word "Evolution" from a set of randomly generated combinations of alphabets (the initial population).

The fitness function is the number of correct alphabets (at correct positions) a particular chromosome has divided by total number of correct alphabets (, i.e., the length of the target word (9)).

Optimal Choices

It was observed that a crossover rate of 90% and Mutation rate of 1% yielded the correct word in the 7th generation - almost half of the 60-10 distribution. The ideal value for crossover is 85%-90% and 0.5%-1% for mutation.